System and method for analyzing data sources to generate metadata

Abstract

A system and method are provided for generating metadata relating to an enterprise management system including at least one data source having one or more of tables and columns. Constraints existing on at least one of the tables and columns in the data source are inferred based on data in the tables and columns. Metadata that includes information on the inferred constraints is generated.

Description

BACKGROUND OF THE INVENTION[0001]1. Field of the Invention[0002]The present invention relates generally to systems and methods for analyzing data sources to generate metadata, (i.e., data about the underlying data). More particularly, the present invention relates to systems and methods for generating metadata from disparate data sources by inferring data relationships and rules based on the data in the disparate data sources and / or query and procedure code.[0003]2. Description of the Related Art[0004]Databases are well known and are used to store and maintain information for almost every conceivable application. The use of databases has become increasingly prevalent over the last few decades; and, today, the vast majority of business records are maintained in databases.[0005]For large businesses, database information can include thousands of different kinds of information and often will range from millions to billions of records. Such businesses seek to leverage their data to make ...

AI Technical Summary

Problems solved by technology

However, most business analysts, managers and corporate decision makers are not knowledgeable of the SQL language and are also not familiar with the data model.

As a result, the primary users of such BI tools are often unable to access the data directly.

In addition, information technology staffs are often unable to successfully use SQL to retrieve the desired data because the user must have an intimate knowledge of the data model used to create the database in order to include the correct commands in a query to retrieve the required data from the tables.

Such problems are also commonly encountered with the use of commercial off-the-shelf products (COTS), as well as BI software applications that have been custom developed for an organization.

In the instance of custom developed software, once the original designers have departed from the organization, it is frequently the case that no one remains with intimate knowledge of the data model used to create the custom software and often either associated documentation is missing or there is minimal informative documentation available for the customized software.

BI tool manufacturers face the challenge of enabling business users to query the data by hiding the complexities of SQL and allowing users to access information without an intimate knowledge of the underlying data model.

However, constraints need not be explicitly defined as part of the data model.

However, for security reasons, the creators of the new tables may not be granted privileges to create a referential integrity constraint on these existing tables.

However, most RDBMS generally do not allow creation of referential integrity constraints across multiple database instances.c. Most constraints such as the NOT NULL constraint, and referential integrity constraints are implemented at the source of the data itself.

Certain constraints are not implemented consistently across RDBMS vendors.

Thus, to maintain portability across database vendors or because the implemented semantics may not be ideally suited to the actual requirement, such constraints may not be explicitly defined as part of the data model, but rather are implemented instead at the sources of the data.e.

In the case of very large tables, explicit definition of referential integrity constraints may slow the insert, update or delete operations.

Most COTS packages have predefined data models that cannot be modified.

As applications get larger and more complex and interface with a large number of other applications, the explicit data model defined in catalog tables becomes less sufficient to populate the metadata layer for a BI system.

This is a commonly encountered problem during BI implementations.

A large amount of time and effort is spent in obtaining a global view of the data model that is not necessarily available through the relational catalogs.

However, such techniques are not sufficient and the capture of the enterprise data model is a largely manual process and involves the BI implementers obtaining information from various sources within the organization.

A lack of knowledge about the implicit data model within an organization often hinders this effort.

Consequently, populating the metadata layer of BI tools becomes a time consuming, expensive and onerous process.

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